A general theoretical framework for interpreting patient-reported outcomes estimated from ordinally scaled item responses
A general theoretical framework for interpreting patient-reported outcomes estimated from ordinally scaled item responses
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DOI：
10.1177/0962280213476380
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发表时间：
2014-10-01
影响因子：
2.3
通讯作者：
Massof, Robert W.
中科院分区：
文献类型：
--
作者：
Massof, Robert W.
一个简单的理论框架解释了病人对量表问卷中项目的反应。固定潜在变量将每个患者和每个项目定位在相同的线性尺度上。项目响应由一组固定的类别阈值控制，每个顺序响应类别对应一个阈值。患者的项目响应是患者变量与患者对项目变量的估计之间的差异的幅度估计，相对于他/她个人定义的响应类别阈值。患者在项目变量的个人估计和类别阈值的个人选择中的差异由添加到相应固定变量的随机变量表示。干预的效果对应于患者变量、患者的反应偏差和/或项目子集的潜在项目变量的变化。通过假设随机变量具有恒定标量协方差矩阵的正态分布来模拟对患者项目反应的干预效果。Rasch分析用于从模拟响应估计潜变量。模拟表明，患者变量的变化和响应偏差的变化对项目响应产生难以区分的影响，并且仅表现为估计的患者变量的变化。项目变量子集的变化表现为干预特定的差异项目功能和估计的个人变量的变化，等于项目变量变化的平均值。模拟表明，干预特定的差异项目功能产生的计算机自适应测试的效率和不准确性。
A simple theoretical framework explains patient responses to items in rating scale questionnaires. Fixed latent variables position each patient and each item on the same linear scale. Item responses are governed by a set of fixed category thresholds, one for each ordinal response category. A patient's item responses are magnitude estimates of the difference between the patient variable and the patient's estimate of the item variable, relative to his/her personally defined response category thresholds. Differences between patients in their personal estimates of the item variable and in their personal choices of category thresholds are represented by random variables added to the corresponding fixed variables. Effects of intervention correspond to changes in the patient variable, the patient's response bias, and/or latent item variables for a subset of items. Intervention effects on patients' item responses were simulated by assuming the random variables are normally distributed with a constant scalar covariance matrix. Rasch analysis was used to estimate latent variables from the simulated responses. The simulations demonstrate that changes in the patient variable and changes in response bias produce indistinguishable effects on item responses and manifest as changes only in the estimated patient variable. Changes in a subset of item variables manifest as intervention-specific differential item functioning and as changes in the estimated person variable that equals the average of changes in the item variables. Simulations demonstrate that intervention-specific differential item functioning produces inefficiencies and inaccuracies in computer adaptive testing.